System and Process to Create a Lookalike Model for a Target Audience to Deliver Advertisements

ABSTRACT

A system and process to create a lookalike model for a target audience to deliver advertisements are disclosed. According to one embodiment, the method comprises selecting survey data from a survey database that relates to an advertisement. A heterogenous treatment effect (HETE) model is trained on the survey data. Persuadable customers are identified from the survey database for the advertisement based on the HETE model. An optimized customer list is generated using personally identifiable information.

FIELD

The present disclosure relates in general to the field of computersoftware and systems, and in particular, to a system and process tocreate a lookalike model for a target audience to deliveradvertisements.

BACKGROUND

Networking websites have become highly prevalent with the ability of theInternet to connect people from all over the world. These networkingwebsites offer advertisements to their users. In order to improveadvertising on networking websites, the networking websites allowadvertisers to target particular demographics within their usercommunity.

A lookalike audience is a way to reach new people who are likely to beinterested in a particular business' advertising because the lookalikeaudience is similar to the advertiser's best existing customers. When anadvertiser creates a lookalike audience, the advertiser chooses a sourceaudience (e.g., a list of persons drawn from, for example pixel data,mobile app data or fans of the advertiser's networking webpage).

Networking websites then identify the common qualities of the people inthe lookalike audience (ex: demographic information or interests). Theyfind people in their community who are similar to (or “look like”) thelookalike audience.

An advertiser can choose the size of a lookalike audience and possibly acountry during the creation process. Smaller audiences more closelymatch the source audience. Creating a larger audience increases anadvertiser's potential reach, but reduces the level of similaritybetween the lookalike audience and the source audience. An advertisercan select countries for its lookalike audiences. Multiple lookalikeaudiences may be generated from a single source audience. Multiplelookalike audiences may be used at the same time for a single ad set.

Advertisers, however, have struggled in identifying the best sourceaudience as a foundation for their lookalike audiences resulting inineffective and costly advertising.

SUMMARY

A system and process to create a lookalike model for a target audienceto deliver advertisements are disclosed. According to one embodiment,the method comprises selecting survey data from a survey database thatrelates to an advertisement. A heterogenous treatment effect (HETE)model is trained on the survey data. Persuadable customers areidentified from the survey database for the advertisement based on theHETE model. An optimized customer list is generated using personallyidentifiable information.

The above and other preferred features, including various novel detailsof implementation and combination of elements, will now be moreparticularly described with reference to the accompanying drawings andpointed out in the claims. It will be understood that the particularmethods and apparatuses are shown by way of illustration only and not aslimitations. As will be understood by those skilled in the art, theprinciples and features explained herein may be employed in various andnumerous embodiments.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, which are included as part of the presentspecification, illustrate the various embodiments of the presentlydisclosed system and method and together with the general descriptiongiven above and the detailed description of the embodiments given belowserve to explain and teach the principles of the present system andmethod.

FIG. 1 illustrates a targeted advertising process as commonly used withnetworking platforms.

FIG. 2 illustrates an exemplary targeting system architecture thatoptimizes target audiences, according to one embodiment.

FIG. 3 illustrates an exemplary persuadable audience targeting process,according to one embodiment.

While the present disclosure is subject to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and will herein be described in detail. Thepresent disclosure should be understood to not be limited to theparticular forms disclosed, but on the contrary, the intention is tocover all modifications, equivalents, and alternatives falling withinthe spirit and scope of the present disclosure.

DETAILED DESCRIPTION

A system and process to create a lookalike model for a target audienceto deliver advertisements are disclosed. According to one embodiment,the method comprises selecting survey data from a survey database thatrelates to an advertisement. A heterogenous treatment effect (HETE)model is trained on the survey data. Persuadable customers areidentified from the survey database for the advertisement based on theHETE model. An optimized customer list is generated using personallyidentifiable information.

The following disclosure provides many different embodiments, orexamples, for implementing different features of the subject matter.Specific examples of components and arrangements are described below tosimplify the present disclosure. These are, of course, merely examplesand are not intended to be limiting. In addition, the present disclosuremay repeat reference numerals and/or letters in the various examples.This repetition is for the purpose of simplicity and clarity and doesnot in itself dictate a relationship between the various embodimentsand/or configurations discussed.

FIG. 1 illustrates a targeted advertising process 100 as commonly usedwith networking sites. Generally, an advertiser (e.g., a retail store, amovie theater chain, a restaurant, etc.) collects information about itscustomers. The advertiser may have customer information, such ascustomer e-mail addresses, phone numbers, first name, last name, zipcodes, city, state, country, date of birth, year of birth, gender, age,mobile advertising ID and a universal ID. This information may have beengathered through a loyalty program, or a registration process.

The advertiser generates a list having some or all of its customerinformation and uploads it to a networking website (e.g., a socialnetworking website, a business networking website, a dating website,etc.) through the networking website's application programming interface(API). (110) The customer information sent to the networking websitemight be a list of the advertiser's best customers. Once the networkingwebsite receives the customer information, it uses the customerinformation as a source audience to generate a lookalike audience. (120)The networking website then serves the advertiser's advertising on thelookalike audience as its target audience (e.g., members of thenetworking website) most similar to the customer list. (130)

FIG. 2 illustrates an exemplary targeting system architecture 200 thatoptimizes target audiences, according to one embodiment. The purpose oftargeting system 200 is to create a lookalike model which will directadvertisements to networking website users who will be most persuaded bythem. Targeting system 200 creates a lookalike model by training aheterogeneous treatment effects (HETE) model on survey experiment dataand using that model to estimate treatment effects for individuals in adatabase of survey takers.

The survey experiment data may include answers to a number of questionsthat are predictive of a user's persuadability (e.g., age, gender,political beliefs, education, and so forth referred to as predictorquestions). The survey shows the survey taker either the advertisementfor targeting, or an unrelated advertisement (e.g., a public serviceannouncement about being polite on the subway). The survey asks if thesurvey taker is likely to take the action of interest in the near future(e.g. “How likely are you to purchase product X in the next month?”known as the final question). The answers to the predictor questions anddifferences in the final question between groups, predict if surveytakers are persuaded by the advertisement.

Specifically, audience optimization server 230 trains the HETE modelusing survey experiment data stored in survey database 210 and customerinformation from advertiser server 220. A heterogenous treatment effect(HETE) model is a class of supervised machine learning model intended toestimate the probability that an individual would be influenced by sometreatment, as determined by unique characteristics of that individual.

Survey database 210 collects information about survey takers such astheir e-mail addresses, phone numbers, first name last name, zip codes,city, state, country, date of birth, year of birth, gender, age, mobileadvertising ID and a universal ID. Survey database 210 may also collectinformation, such as purchase history and other data about customerpurchases including price and date.

Targeting system 200 also includes an advertiser server 220, where theadvertiser server 220 includes advertisements for testing. Targetingsystem 200 also includes networking server 240 that provides anetworking website (e.g., social or business networking platform) thatserves advertising for the advertiser based on the optimized customerinformation generated by audience optimization server 230.

Targeting system 200 generates a list of most persuadable persons 250 onthe networking server 240. Targeting system 200 is superior to priorsystems as reflected in FIG. 1 that generate audiences that can only becustomized by the information that people volunteer (e.g., this mightinclude someone's likes of content on the networking website ornetworking website pages they subscribe to). Targeting system 200determines the type of messaging that is likely to persuade a user ofthe networking website. A network (e.g., Internet, wireless, etc.)connects all the individual components of targeting system 200. Audienceoptimization server 230 determines optimized customer information thatis used by networking server 240 to determine the persuadable audience250. The persuadable audience 250 is a subset of users of the networkingwebsite to whom the advertiser will target advertisements moreeffectively because the audience is more persuadable.

To optimize an advertisement, instead of only which people, a surveyexperiment is run with several different treatment groups—e.g., group 1sees the ad with the unicorn, group 2 sees the ad with the beach, group3 sees the ad with the mountain climbers, and group 4 sees the PSA (thecontrol). Determining which advertisements a person finds mostpersuasive can be done in two ways. First, with answers to directquestions asked on the survey itself (e.g., “If you could, would youlike to run away from it all and live on a sailboat?”). Direct questionstend to be most predictive. Second, enough PII is provided that a surveytaker can be identified in other data (e.g., national consumer file),and use data or models from that.

Audience optimization server 230 uses HETE modeling to correctlyrank-order individuals by treatment effect. (e.g., the output scores aresuch that if individual A has a higher score than individual B, thenindividual A should have a larger probability of responding to treatmentthan individual B.)

Audience optimization server 230 takes a list of the most persuadablesurvey respondents from the survey database 210, appends theirpersonally identifiable information to it (e.g., e-mail addresses andnames received from survey takers directly), and uploads it to thenetworking server 240 using its API. In alternate embodiments, thisupload may also be manual—e.g., someone sending a file of names andtelephone numbers through the service's GUI. Advertisers may theninstruct networking server 240 to build a lookalike model off that seedlist of people (e.g., optimized customer information), and find morepeople on the networking platform that look like the persuadable targets(e.g., persuadable audience 250) and advertise to them.

Audience optimization server 230 allows on an individual basis, to knowwho (e.g., which of the advertiser's customers) is most likely to bepersuaded by which message. Sometimes the most popular message overallis less effective, or even counterproductive, for certainsub-populations and audience optimization server 230 uses HETE modelingto predict that.

FIG. 3 illustrates an exemplary persuadable audience targeting process300, according to one embodiment. Audience optimization server 230selects survey data from survey database 210 that relates to anadvertisement (310). Audience optimization server 230 trains a HETEmodel on the selected survey data (320). Audience optimization server230 identifies the most persuadable customers from survey database 210for the particular advertisement based on the HETE model (330).

Audience optimization server 230 generates an optimized customer listusing the personally identifiable information for individuals selectedfrom the survey database (340). Audience optimization server 230 uploadsthe optimized customer list to the networking server 240 (350).According to another embodiment, advertiser server 220 uploads theoptimized customer list to the networking server 240. Networking server240 generates a list of persuadable audience 250 (360) and serves theadvertisement on networking site to the persuadable audience 250 (370).

While the present disclosure has been described in terms of particularembodiments and applications, summarized form, it is not intended thatthese descriptions in any way limit its scope to any such embodimentsand applications, and it will be understood that many substitutions,changes and variations in the described embodiments, applications anddetails of the method and system illustrated herein and of theiroperation can be made by those skilled in the art without departing fromthe scope of the present disclosure.

What is claimed is:
 1. A method, comprising: selecting survey data froma survey database that relates to an advertisement; training a model topredict heterogeneous treatment effects (HETE) on the survey data;identifying persuadable customers from the survey database for theadvertisement based on the model; and generating a customer list usingpersonally identifiable information for persons in the survey database.2. The method of claim 1, further comprising directing the advertisementto networking website users who share characteristics with customers onthe customer list.
 3. The method of claim 1, wherein the survey dataincludes answers to one or more questions that are predictive of auser's persuadability.
 4. The method of claim 3, wherein the survey dataincludes one or more of age, gender, political beliefs, and education.5. The method of claim 1, wherein the survey data results from a surveythat shows a survey taker one of the advertisement for targeting, or anunrelated advertisement.
 6. The method of claim 1, wherein the surveyasks if the survey taker is likely to take an action of interest in apredetermined timeframe as a final question.
 7. The method of claim 6,wherein the survey data and differences in the final question betweengroups of survey takers, predict if the survey takers are persuaded bythe advertisement.
 8. The method of claim 1, wherein the model is aclass of supervised machine learning model that estimates a probabilitythat an individual would be influenced by an advertisement, asdetermined by unique characteristics of that individual.
 9. The methodof claim 1, further comprising running a survey experiment on severaldifferent treatment groups each being shown a different advertisement.10. The method of claim 1, wherein the customer list is rank-ordered bytreatment effect.
 11. A non-transitory computer readable mediumcontaining computer-readable instructions stored therein for causing acomputer processor to perform operations comprising: selecting surveydata from a survey database that relates to an advertisement; training amodel to predict heterogeneous treatment effects (HETE) on the surveydata; identifying persuadable customers from the survey database for theadvertisement based on the model; and generating a customer list usingpersonally identifiable information for persons in the survey database.12. The computer readable medium of claim 11, further includingadditional instructions comprising directing the advertisement tonetworking website users who share characteristics with customers on thecustomer list.
 13. The computer readable medium of claim 11, wherein thesurvey data includes answers to one or more questions that arepredictive of persuadability.
 14. The computer readable medium of claim13, wherein the survey data includes one or more of age, gender,political beliefs, and education.
 15. The computer readable medium ofclaim 11, wherein the survey data results from a survey that shows asurvey taker one of the advertisement for targeting, or an unrelatedadvertisement.
 16. The computer readable medium of claim 11, wherein thesurvey asks if the survey taker is likely to take an action of interestin a predetermined timeframe as a final question.
 17. The computerreadable medium of claim 16, wherein the survey data and differences inthe final question between groups of survey takers, predict if thesurvey takers are persuaded by the advertisement.
 18. The computerreadable medium of claim 11, wherein the model is a class of supervisedmachine learning model that estimates a probability that an individualwould be influenced by an advertisement, as determined by uniquecharacteristics of that individual.
 19. The computer readable medium ofclaim 11, further including additional instructions comprising running asurvey experiment on several different treatment groups each being showna different advertisement.
 20. The computer readable medium of claim 11,wherein the customer list is rank-ordered by treatment effect.